{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from collections import defaultdict\n",
    "\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from Bio.PopGen.GenePop import Controller as gpc\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "my_pops = [l.rstrip() for l in open('hapmap10_auto_noofs_2.pops')]\n",
    "num_pops = len(my_pops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ctrl = gpc.GenePopController()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(multi_fis, multi_fst, multi_fit), f_iter = ctrl.calc_fst_all('hapmap10_auto_noofs_2.gp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0008 0.1074 0.1081\n"
     ]
    }
   ],
   "source": [
    "print(multi_fis, multi_fst, multi_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fst_vals = []\n",
    "fis_vals = []\n",
    "fit_vals = []\n",
    "for f_case in f_iter:\n",
    "    name, fis, fst, fit, qinter, qintra = f_case\n",
    "    fst_vals.append(fst)\n",
    "    fis_vals.append(fis)\n",
    "    fit_vals.append(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.15, 0.4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "fig = plt.figure(figsize=(16, 9))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.hist(fst_vals, 50, color='r')\n",
    "ax.set_title('FST, FIS and FIT distributions')\n",
    "ax.set_xlabel('FST')\n",
    "ax = fig.add_subplot(2, 3, 2)\n",
    "sns.violinplot([fis_vals], ax=ax, vert=False)\n",
    "ax.set_yticklabels(['FIS'])\n",
    "ax.set_xlim(-.15, 0.4)\n",
    "ax = fig.add_subplot(2, 3, 3)\n",
    "sns.violinplot([fit_vals], ax=ax, vert=False)\n",
    "ax.set_yticklabels(['FIT'])\n",
    "ax.set_xlim(-.15, 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpair_iter, avg = ctrl.calc_fst_pair('hapmap10_auto_noofs_2.gp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "min_pair = min(avg.values())\n",
    "max_pair = max(avg.values())\n",
    "arr = np.ones((num_pops - 1, num_pops - 1, 3), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'ASW'),\n",
       " Text(0,0,'CEU'),\n",
       " Text(0,0,'CHB'),\n",
       " Text(0,0,'CHD'),\n",
       " Text(0,0,'GIH'),\n",
       " Text(0,0,'JPT'),\n",
       " Text(0,0,'LWK'),\n",
       " Text(0,0,'MEX'),\n",
       " Text(0,0,'MKK'),\n",
       " Text(0,0,'TSI')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "fig = plt.figure(figsize=(16,9))\n",
    "ax = fig.add_subplot(111)\n",
    "for row in range(num_pops - 1):\n",
    "    for col in range(row + 1, num_pops):\n",
    "        val = avg[(col, row)]\n",
    "        norm_val = (val - min_pair) / (max_pair - min_pair)\n",
    "        ax.text(col - 1, row, '%.3f' % val, ha='center')\n",
    "        if norm_val == 0.0:\n",
    "            arr[row, col - 1, 0] = 1\n",
    "            arr[row, col - 1, 1] = 1\n",
    "            arr[row, col - 1, 2] = 0\n",
    "        elif norm_val == 1.0:\n",
    "            arr[row, col - 1, 0] = 1\n",
    "            arr[row, col - 1, 1] = 0\n",
    "            arr[row, col - 1, 2] = 1\n",
    "        else:\n",
    "            arr[row, col - 1, 0] = 1 - norm_val\n",
    "            arr[row, col - 1, 1] = 1\n",
    "            arr[row, col - 1, 2] = 1\n",
    "ax.imshow(arr, interpolation='none')\n",
    "ax.set_title('Multilocus Pairwise FST')\n",
    "ax.set_xticks(range(num_pops - 1))\n",
    "ax.set_xticklabels(my_pops[1:])\n",
    "ax.set_yticks(range(num_pops - 1))\n",
    "ax.set_yticklabels(my_pops[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pop_ceu = my_pops.index('CEU')\n",
    "pop_yri = my_pops.index('YRI')\n",
    "start_pos = 136261886  # b36\n",
    "end_pos = 136350481\n",
    "all_fsts = []\n",
    "inside_fsts = []\n",
    "for locus_pfst in fpair_iter:\n",
    "    name = locus_pfst[0]\n",
    "    pfst = locus_pfst[1]\n",
    "    pos = int(name.split('/')[-1])  # dependent\n",
    "    my_fst = pfst[(pop_yri, pop_ceu)]\n",
    "    if my_fst == '-':  # Can be this\n",
    "        continue\n",
    "    all_fsts.append(my_fst)\n",
    "    if pos >= start_pos and pos <= end_pos:\n",
    "        inside_fsts.append(my_fst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1106, 0.2485]\n",
      "0.08/0.13/0.33\n"
     ]
    }
   ],
   "source": [
    "print(inside_fsts)\n",
    "print('%.2f/%.2f/%.2f' % (np.median(all_fsts), np.mean(all_fsts), np.percentile(all_fsts, 90)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03660950413223141"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * (end_pos - start_pos) / 242000000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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